


"""simple docstring"""



import argparse
import logging
import os
import re

import tensorflow as tf

from transformers import (
    AutoConfig,
    AutoTokenizer,
    DataCollatorForLanguageModeling,
    PushToHubCallback,
    TFAutoModelForMaskedLM,
    create_optimizer,
)


lowercase_							=							logging.getLogger(__name__)

lowercase_							=							tf.data.AUTOTUNE
def      UpperCAmelCase     (       )						-> str:


	"""simple docstring"""




	lowerCAmelCase_      							=			argparse.ArgumentParser(description='''Train a masked language model on TPU.'''	)
	parser.add_argument(
	    '''--pretrained_model_config'''			,   type=_A			,   default='''roberta-base'''			,   help='''The model config to use. Note that we don\'t copy the model\'s weights, only the config!'''			,   )
	parser.add_argument(
	    '''--tokenizer'''			,   type=_A			,   default='''unigram-tokenizer-wikitext'''			,   help='''The name of the tokenizer to load. We use the pretrained tokenizer to initialize the model\'s vocab size.'''			,   )

	parser.add_argument(
	    '''--per_replica_batch_size'''			,   type=_A			,   default=8			,   help='''Batch size per TPU core.'''			,   )

	parser.add_argument(
	    '''--no_tpu'''			,   action='''store_true'''			,   help='''If set, run on CPU and don\'t try to initialize a TPU. Useful for debugging on non-TPU instances.'''			,   )

	parser.add_argument(
	    '''--tpu_name'''			,   type=_A			,   help='''Name of TPU resource to initialize. Should be blank on Colab, and \'local\' on TPU VMs.'''			,   default='''local'''			,   )

	parser.add_argument(
	    '''--tpu_zone'''			,   type=_A			,   help='''Google cloud zone that TPU resource is located in. Only used for non-Colab TPU nodes.'''			,   )

	parser.add_argument(
	    '''--gcp_project'''			,   type=_A			,   help='''Google cloud project name. Only used for non-Colab TPU nodes.'''	)

	parser.add_argument(
	    '''--bfloat16'''			,   action='''store_true'''			,   help='''Use mixed-precision bfloat16 for training. This is the recommended lower-precision format for TPU.'''			,   )

	parser.add_argument(
	    '''--train_dataset'''			,   type=_A			,   help='''Path to training dataset to load. If the path begins with `gs://`'''
	    ''' then the dataset will be loaded from a Google Cloud Storage bucket.'''			,   )

	parser.add_argument(
	    '''--shuffle_buffer_size'''			,   type=_A			,   default=2**1_8			,   help='''Size of the shuffle buffer (in samples)'''			,   )

	parser.add_argument(
	    '''--eval_dataset'''			,   type=_A			,   help='''Path to evaluation dataset to load. If the path begins with `gs://`'''
	    ''' then the dataset will be loaded from a Google Cloud Storage bucket.'''			,   )

	parser.add_argument(
	    '''--num_epochs'''			,   type=_A			,   default=1			,   help='''Number of epochs to train for.'''			,   )

	parser.add_argument(
	    '''--learning_rate'''			,   type=_A			,   default=1E-4			,   help='''Learning rate to use for training.'''			,   )

	parser.add_argument(
	    '''--weight_decay_rate'''			,   type=_A			,   default=1E-3			,   help='''Weight decay rate to use for training.'''			,   )

	parser.add_argument(
	    '''--max_length'''			,   type=_A			,   default=5_1_2			,   help='''Maximum length of tokenized sequences. Should match the setting used in prepare_tfrecord_shards.py'''			,   )

	parser.add_argument(
	    '''--mlm_probability'''			,   type=_A			,   default=0.1_5			,   help='''Fraction of tokens to mask during training.'''			,   )

	parser.add_argument('''--output_dir'''			,   type=_A			,   required=_A			,   help='''Path to save model checkpoints to.'''	)
	parser.add_argument('''--hub_model_id'''			,   type=_A			,   help='''Model ID to upload to on the Hugging Face Hub.'''	)

	lowerCAmelCase_      							=			parser.parse_args()
	return args
def      UpperCAmelCase     (       _lowercase :   Union[str, Any]	)						-> str:


	"""simple docstring"""




	try:
		if args.tpu_name:
			lowerCAmelCase_      							=			tf.distribute.cluster_resolver.TPUClusterResolver(
			    args.tpu_name			,   zone=args.tpu_zone			,   project=args.gcp_project	)
		else:
			lowerCAmelCase_      							=			tf.distribute.cluster_resolver.TPUClusterResolver()
	except ValueError:
		raise RuntimeError(
		    '''Couldn\'t connect to TPU! Most likely you need to specify --tpu_name, --tpu_zone, or '''
		    '''--gcp_project. When running on a TPU VM, use --tpu_name local.'''	)

	tf.config.experimental_connect_to_cluster(_A	)
	tf.tpu.experimental.initialize_tpu_system(_A	)

	return tpu
def      UpperCAmelCase     (       _lowercase :   Union[str, Any]	)						-> Union[str, Any]:


	"""simple docstring"""




	lowerCAmelCase_      							=			0
	for file in file_list:
		lowerCAmelCase_      							=			file.split('''/'''	)[-1]
		lowerCAmelCase_      							=			re.search(r'''-\d+-(\d+)\.tfrecord'''			,   _A	).group(1	)
		lowerCAmelCase_      							=			int(_A	)
		num_samples += sample_count

	return num_samples
def      UpperCAmelCase     (       _lowercase :   List[str]			,   _lowercase :   Optional[Any]			,   _lowercase :   List[Any]			,   _lowercase :   int			,   _lowercase :   Dict			,   _lowercase :   int=None	)						-> List[Any]:


	"""simple docstring"""




	lowerCAmelCase_      							=			count_samples(_A	)
	lowerCAmelCase_      							=			tf.data.Dataset.from_tensor_slices(_A	)
	if shuffle:
		lowerCAmelCase_      							=			dataset.shuffle(len(_A	)	)
	lowerCAmelCase_      							=			tf.data.TFRecordDataset(_A			,   num_parallel_reads=_A	)
	# TF can't infer the total sample count because it doesn't read all the records yet, so we assert it here
	lowerCAmelCase_      							=			dataset.apply(tf.data.experimental.assert_cardinality(_A	)	)
	lowerCAmelCase_      							=			dataset.map(_A			,   num_parallel_calls=_A	)
	if shuffle:
		assert shuffle_buffer_size is not None
		lowerCAmelCase_      							=			dataset.shuffle(args.shuffle_buffer_size	)
	lowerCAmelCase_      							=			dataset.batch(_A			,   drop_remainder=_A	)
	lowerCAmelCase_      							=			dataset.map(_A			,   num_parallel_calls=_A	)
	lowerCAmelCase_      							=			dataset.prefetch(_A	)
	return dataset
def      UpperCAmelCase     (       _lowercase :   Optional[int]	)						-> Optional[int]:


	"""simple docstring"""




	if not args.no_tpu:
		lowerCAmelCase_      							=			initialize_tpu(_A	)
		lowerCAmelCase_      							=			tf.distribute.TPUStrategy(_A	)
	else:
		lowerCAmelCase_      							=			tf.distribute.OneDeviceStrategy(device='''/gpu:0'''	)

	if args.bfloataa:
		tf.keras.mixed_precision.set_global_policy('''mixed_bfloat16'''	)

	lowerCAmelCase_      							=			AutoTokenizer.from_pretrained(args.tokenizer	)
	lowerCAmelCase_      							=			AutoConfig.from_pretrained(args.pretrained_model_config	)
	lowerCAmelCase_      							=			tokenizer.vocab_size

	lowerCAmelCase_      							=			tf.io.gfile.glob(os.path.join(args.train_dataset			,   '''*.tfrecord'''	)	)
	if not training_records:
		raise ValueError(F"""No .tfrecord files found in {args.train_dataset}."""	)
	lowerCAmelCase_      							=			tf.io.gfile.glob(os.path.join(args.eval_dataset			,   '''*.tfrecord'''	)	)
	if not eval_records:
		raise ValueError(F"""No .tfrecord files found in {args.eval_dataset}."""	)

	lowerCAmelCase_      							=			count_samples(_A	)

	lowerCAmelCase_      							=			num_train_samples // (args.per_replica_batch_size * strategy.num_replicas_in_sync)
	lowerCAmelCase_      							=			steps_per_epoch * args.num_epochs

	with strategy.scope():
		lowerCAmelCase_      							=			TFAutoModelForMaskedLM.from_config(_A	)
		model(model.dummy_inputs	)  # Pass some dummy inputs through the model to ensure all the weights are built
		lowerCAmelCase_							,	lowerCAmelCase_      							=			create_optimizer(
		    num_train_steps=_A			,   num_warmup_steps=total_train_steps // 2_0			,   init_lr=args.learning_rate			,   weight_decay_rate=args.weight_decay_rate			,   )

		# Transformers models compute the right loss for their task by default when labels are passed, and will
		# use this for training unless you specify your own loss function in compile().
		model.compile(optimizer=_A			,   metrics=['''accuracy''']	)

	def decode_fn(_lowercase :   Any	):
		lowerCAmelCase_      							=			{
		    '''input_ids''': tf.io.FixedLenFeature(dtype=tf.intaa			,   shape=(args.max_length,)	),
		    '''attention_mask''': tf.io.FixedLenFeature(dtype=tf.intaa			,   shape=(args.max_length,)	),
		}
		return tf.io.parse_single_example(_A			,   _A	)

	# Many of the data collators in Transformers are TF-compilable when return_tensors == "tf", so we can
	# use their methods in our data pipeline.
	lowerCAmelCase_      							=			DataCollatorForLanguageModeling(
	    tokenizer=_A			,   mlm_probability=args.mlm_probability			,   mlm=_A			,   return_tensors='''tf'''	)

	def mask_with_collator(_lowercase :   Dict	):
		# TF really needs an isin() function
		lowerCAmelCase_      							=			(
		    ~tf.cast(batch['''attention_mask''']			,   tf.bool	)
		    | (batch['''input_ids'''] == tokenizer.cls_token_id)
		    | (batch['''input_ids'''] == tokenizer.sep_token_id)
		)
		lowerCAmelCase_							,	lowerCAmelCase_      							=			data_collator.tf_mask_tokens(
		    batch['''input_ids''']			,   vocab_size=len(_A	)			,   mask_token_id=tokenizer.mask_token_id			,   special_tokens_mask=_A			,   )
		return batch

	lowerCAmelCase_      							=			args.per_replica_batch_size * strategy.num_replicas_in_sync

	lowerCAmelCase_      							=			prepare_dataset(
	    _A			,   decode_fn=_A			,   mask_fn=_A			,   batch_size=_A			,   shuffle=_A			,   shuffle_buffer_size=args.shuffle_buffer_size			,   )

	lowerCAmelCase_      							=			prepare_dataset(
	    _A			,   decode_fn=_A			,   mask_fn=_A			,   batch_size=_A			,   shuffle=_A			,   )

	lowerCAmelCase_      							=			[]
	if args.hub_model_id:
		callbacks.append(
		    PushToHubCallback(output_dir=args.output_dir			,   hub_model_id=args.hub_model_id			,   tokenizer=_A	)	)

	model.fit(
	    _A			,   validation_data=_A			,   epochs=args.num_epochs			,   callbacks=_A			,   )

	model.save_pretrained(args.output_dir	)


if __name__ == "__main__":
		lowercase_							=							parse_args()
		main(args)

"""simple docstring"""



import argparse
from collections import OrderedDict
from pathlib import Path

import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision.transforms import functional as F

from transformers import DetrImageProcessor, TableTransformerConfig, TableTransformerForObjectDetection
from transformers.utils import logging


logging.set_verbosity_info()
a       :						Dict       		=		logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
a       :						Dict       		=		[]
for i in range(6):
 # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
 rename_keys.append(
     (F"transformer.encoder.layers.{i}.self_attn.out_proj.weight", F"encoder.layers.{i}.self_attn.out_proj.weight")
 )
 rename_keys.append(
     (F"transformer.encoder.layers.{i}.self_attn.out_proj.bias", F"encoder.layers.{i}.self_attn.out_proj.bias")
 )
 rename_keys.append((F"transformer.encoder.layers.{i}.linear1.weight", F"encoder.layers.{i}.fc1.weight"))
 rename_keys.append((F"transformer.encoder.layers.{i}.linear1.bias", F"encoder.layers.{i}.fc1.bias"))
 rename_keys.append((F"transformer.encoder.layers.{i}.linear2.weight", F"encoder.layers.{i}.fc2.weight"))
 rename_keys.append((F"transformer.encoder.layers.{i}.linear2.bias", F"encoder.layers.{i}.fc2.bias"))
 rename_keys.append(
     (F"transformer.encoder.layers.{i}.norm1.weight", F"encoder.layers.{i}.self_attn_layer_norm.weight")
 )
 rename_keys.append((F"transformer.encoder.layers.{i}.norm1.bias", F"encoder.layers.{i}.self_attn_layer_norm.bias"))
 rename_keys.append((F"transformer.encoder.layers.{i}.norm2.weight", F"encoder.layers.{i}.final_layer_norm.weight"))
 rename_keys.append((F"transformer.encoder.layers.{i}.norm2.bias", F"encoder.layers.{i}.final_layer_norm.bias"))
 # decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
 rename_keys.append(
     (F"transformer.decoder.layers.{i}.self_attn.out_proj.weight", F"decoder.layers.{i}.self_attn.out_proj.weight")
 )
 rename_keys.append(
     (F"transformer.decoder.layers.{i}.self_attn.out_proj.bias", F"decoder.layers.{i}.self_attn.out_proj.bias")
 )
 rename_keys.append(
     (
         F"transformer.decoder.layers.{i}.multihead_attn.out_proj.weight",
         F"decoder.layers.{i}.encoder_attn.out_proj.weight",
     )
 )
 rename_keys.append(
     (
         F"transformer.decoder.layers.{i}.multihead_attn.out_proj.bias",
         F"decoder.layers.{i}.encoder_attn.out_proj.bias",
     )
 )
 rename_keys.append((F"transformer.decoder.layers.{i}.linear1.weight", F"decoder.layers.{i}.fc1.weight"))
 rename_keys.append((F"transformer.decoder.layers.{i}.linear1.bias", F"decoder.layers.{i}.fc1.bias"))
 rename_keys.append((F"transformer.decoder.layers.{i}.linear2.weight", F"decoder.layers.{i}.fc2.weight"))
 rename_keys.append((F"transformer.decoder.layers.{i}.linear2.bias", F"decoder.layers.{i}.fc2.bias"))
 rename_keys.append(
     (F"transformer.decoder.layers.{i}.norm1.weight", F"decoder.layers.{i}.self_attn_layer_norm.weight")
 )
 rename_keys.append((F"transformer.decoder.layers.{i}.norm1.bias", F"decoder.layers.{i}.self_attn_layer_norm.bias"))
 rename_keys.append(
     (F"transformer.decoder.layers.{i}.norm2.weight", F"decoder.layers.{i}.encoder_attn_layer_norm.weight")
 )
 rename_keys.append(
     (F"transformer.decoder.layers.{i}.norm2.bias", F"decoder.layers.{i}.encoder_attn_layer_norm.bias")
 )
 rename_keys.append((F"transformer.decoder.layers.{i}.norm3.weight", F"decoder.layers.{i}.final_layer_norm.weight"))
 rename_keys.append((F"transformer.decoder.layers.{i}.norm3.bias", F"decoder.layers.{i}.final_layer_norm.bias"))

# convolutional projection + query embeddings + layernorm of encoder + layernorm of decoder + class and bounding box heads
rename_keys.extend(
    [
        ('''input_proj.weight''', '''input_projection.weight'''),
        ('''input_proj.bias''', '''input_projection.bias'''),
        ('''query_embed.weight''', '''query_position_embeddings.weight'''),
        ('''transformer.encoder.norm.weight''', '''encoder.layernorm.weight'''),
        ('''transformer.encoder.norm.bias''', '''encoder.layernorm.bias'''),
        ('''transformer.decoder.norm.weight''', '''decoder.layernorm.weight'''),
        ('''transformer.decoder.norm.bias''', '''decoder.layernorm.bias'''),
        ('''class_embed.weight''', '''class_labels_classifier.weight'''),
        ('''class_embed.bias''', '''class_labels_classifier.bias'''),
        ('''bbox_embed.layers.0.weight''', '''bbox_predictor.layers.0.weight'''),
        ('''bbox_embed.layers.0.bias''', '''bbox_predictor.layers.0.bias'''),
        ('''bbox_embed.layers.1.weight''', '''bbox_predictor.layers.1.weight'''),
        ('''bbox_embed.layers.1.bias''', '''bbox_predictor.layers.1.bias'''),
        ('''bbox_embed.layers.2.weight''', '''bbox_predictor.layers.2.weight'''),
        ('''bbox_embed.layers.2.bias''', '''bbox_predictor.layers.2.bias'''),
    ]
)


def 			_UpperCamelCase						(    _A       ,   _A       ,   _A   )     ->	Tuple:





    """simple docstring"""

    _UpperCAmelCase        = state_dict.pop(_A   )
    _UpperCAmelCase        = val


def 			_UpperCamelCase						(    _A   )     ->	str:





    """simple docstring"""

    _UpperCAmelCase        = OrderedDict()
    for key, value in state_dict.items():
        if "backbone.0.body" in key:
            _UpperCAmelCase        = key.replace("""backbone.0.body"""       ,   """backbone.conv_encoder.model"""   )
            _UpperCAmelCase        = value
        else:
            _UpperCAmelCase        = value

    return new_state_dict


def 			_UpperCamelCase						(    _A   )     ->	int:





    """simple docstring"""

    _UpperCAmelCase        = """"""

    # first: transformer encoder
    for i in range(6   ):
        # read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
        _UpperCAmelCase        = state_dict.pop(F"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"""   )
        _UpperCAmelCase        = state_dict.pop(F"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias"""   )
        # next, add query, keys and values (in that order) to the state dict
        _UpperCAmelCase        = in_proj_weight[:2_5_6, :]
        _UpperCAmelCase        = in_proj_bias[:2_5_6]
        _UpperCAmelCase        = in_proj_weight[2_5_6:5_1_2, :]
        _UpperCAmelCase        = in_proj_bias[2_5_6:5_1_2]
        _UpperCAmelCase        = in_proj_weight[-2_5_6:, :]
        _UpperCAmelCase        = in_proj_bias[-2_5_6:]
    # next: transformer decoder (which is a bit more complex because it also includes cross-attention)
    for i in range(6   ):
        # read in weights + bias of input projection layer of self-attention
        _UpperCAmelCase        = state_dict.pop(F"""{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight"""   )
        _UpperCAmelCase        = state_dict.pop(F"""{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_bias"""   )
        # next, add query, keys and values (in that order) to the state dict
        _UpperCAmelCase        = in_proj_weight[:2_5_6, :]
        _UpperCAmelCase        = in_proj_bias[:2_5_6]
        _UpperCAmelCase        = in_proj_weight[2_5_6:5_1_2, :]
        _UpperCAmelCase        = in_proj_bias[2_5_6:5_1_2]
        _UpperCAmelCase        = in_proj_weight[-2_5_6:, :]
        _UpperCAmelCase        = in_proj_bias[-2_5_6:]
        # read in weights + bias of input projection layer of cross-attention
        _UpperCAmelCase        = state_dict.pop(
            F"""{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight"""   )
        _UpperCAmelCase        = state_dict.pop(F"""{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_bias"""   )
        # next, add query, keys and values (in that order) of cross-attention to the state dict
        _UpperCAmelCase        = in_proj_weight_cross_attn[:2_5_6, :]
        _UpperCAmelCase        = in_proj_bias_cross_attn[:2_5_6]
        _UpperCAmelCase        = in_proj_weight_cross_attn[2_5_6:5_1_2, :]
        _UpperCAmelCase        = in_proj_bias_cross_attn[2_5_6:5_1_2]
        _UpperCAmelCase        = in_proj_weight_cross_attn[-2_5_6:, :]
        _UpperCAmelCase        = in_proj_bias_cross_attn[-2_5_6:]


def 			_UpperCamelCase						(    _A       ,   _A   )     ->	Optional[int]:





    """simple docstring"""

    _UpperCAmelCase     ,_UpperCAmelCase        = image.size
    _UpperCAmelCase        = max(_A       ,   _A   )
    _UpperCAmelCase        = 8_0_0 if """detection""" in checkpoint_url else 1_0_0_0
    _UpperCAmelCase        = target_max_size / current_max_size
    _UpperCAmelCase        = image.resize((int(round(scale * width   )   ), int(round(scale * height   )   ))   )

    return resized_image


def 			_UpperCamelCase						(    _A   )     ->	str:





    """simple docstring"""

    _UpperCAmelCase        = F.to_tensor(_A   )
    _UpperCAmelCase        = F.normalize(_A       ,   mean=[0.485, 0.456, 0.406]       ,   std=[0.229, 0.224, 0.225]   )
    return image


@torch.no_grad()
def 			_UpperCamelCase						(    _A       ,   _A       ,   _A   )     ->	str:





    """simple docstring"""

    logger.info("""Converting model..."""   )

    # load original state dict
    _UpperCAmelCase        = torch.hub.load_state_dict_from_url(_A       ,   map_location="""cpu"""   )
    # rename keys
    for src, dest in rename_keys:
        rename_key(_A       ,   _A       ,   _A   )
    _UpperCAmelCase        = rename_backbone_keys(_A   )
    # query, key and value matrices need special treatment
    read_in_q_k_v(_A   )
    # important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
    _UpperCAmelCase        = """model."""
    for key in state_dict.copy().keys():
        if not key.startswith("""class_labels_classifier"""   ) and not key.startswith("""bbox_predictor"""   ):
            _UpperCAmelCase        = state_dict.pop(_A   )
            _UpperCAmelCase        = val
    # create HuggingFace model and load state dict
    _UpperCAmelCase        = TableTransformerConfig(
        backbone="""resnet18"""       ,   mask_loss_coefficient=1       ,   dice_loss_coefficient=1       ,   ce_loss_coefficient=1       ,   bbox_loss_coefficient=5       ,   giou_loss_coefficient=2       ,   eos_coefficient=0.4       ,   class_cost=1       ,   bbox_cost=5       ,   giou_cost=2       ,   )

    if "detection" in checkpoint_url:
        _UpperCAmelCase        = 1_5
        _UpperCAmelCase        = 2
        _UpperCAmelCase        = {0: """table""", 1: """table rotated"""}
        _UpperCAmelCase        = idalabel
        _UpperCAmelCase        = {v: k for k, v in idalabel.items()}
    else:
        _UpperCAmelCase        = 1_2_5
        _UpperCAmelCase        = 6
        _UpperCAmelCase        = {
            0: """table""",
            1: """table column""",
            2: """table row""",
            3: """table column header""",
            4: """table projected row header""",
            5: """table spanning cell""",
        }
        _UpperCAmelCase        = idalabel
        _UpperCAmelCase        = {v: k for k, v in idalabel.items()}

    _UpperCAmelCase        = DetrImageProcessor(
        format="""coco_detection"""       ,   max_size=8_0_0 if """detection""" in checkpoint_url else 1_0_0_0   )
    _UpperCAmelCase        = TableTransformerForObjectDetection(_A   )
    model.load_state_dict(_A   )
    model.eval()

    # verify our conversion
    _UpperCAmelCase        = """example_pdf.png""" if """detection""" in checkpoint_url else """example_table.png"""
    _UpperCAmelCase        = hf_hub_download(repo_id="""nielsr/example-pdf"""       ,   repo_type="""dataset"""       ,   filename=_A   )
    _UpperCAmelCase        = Image.open(_A   ).convert("""RGB"""   )
    _UpperCAmelCase        = normalize(resize(_A       ,   _A   )   ).unsqueeze(0   )

    _UpperCAmelCase        = model(_A   )

    if "detection" in checkpoint_url:
        _UpperCAmelCase        = (1, 1_5, 3)
        _UpperCAmelCase        = torch.tensor(
            [[-6.7_897, -16.9_985, 6.7_937], [-8.0_186, -22.2_192, 6.9_677], [-7.3_117, -21.0_708, 7.4_055]]   )
        _UpperCAmelCase        = torch.tensor([[0.4_867, 0.1_767, 0.6_732], [0.6_718, 0.4_479, 0.3_830], [0.4_716, 0.1_760, 0.6_364]]   )

    else:
        _UpperCAmelCase        = (1, 1_2_5, 7)
        _UpperCAmelCase        = torch.tensor(
            [[-18.1_430, -8.3_214, 4.8_274], [-18.4_685, -7.1_361, -4.2_667], [-26.3_693, -9.3_429, -4.9_962]]   )
        _UpperCAmelCase        = torch.tensor([[0.4_983, 0.5_595, 0.9_440], [0.4_916, 0.6_315, 0.5_954], [0.6_108, 0.8_637, 0.1_135]]   )

    assert outputs.logits.shape == expected_shape
    assert torch.allclose(outputs.logits[0, :3, :3]       ,   _A       ,   atol=1e-4   )
    assert torch.allclose(outputs.pred_boxes[0, :3, :3]       ,   _A       ,   atol=1e-4   )
    print("""Looks ok!"""   )

    if pytorch_dump_folder_path is not None:
        # Save model and image processor
        logger.info(F"""Saving PyTorch model and image processor to {pytorch_dump_folder_path}..."""   )
        Path(_A   ).mkdir(exist_ok=_A   )
        model.save_pretrained(_A   )
        image_processor.save_pretrained(_A   )

    if push_to_hub:
        # Push model to HF hub
        logger.info("""Pushing model to the hub..."""   )
        _UpperCAmelCase        = (
            """microsoft/table-transformer-detection"""
            if """detection""" in checkpoint_url
            else """microsoft/table-transformer-structure-recognition"""
        )
        model.push_to_hub(_A   )
        image_processor.push_to_hub(_A   )


if __name__ == "__main__":
 a       :						Dict       		=		argparse.ArgumentParser()

 parser.add_argument(
     '''--checkpoint_url''',
     default='''https://pubtables1m.blob.core.windows.net/model/pubtables1m_detection_detr_r18.pth''',
     type=str,
     choices=[
         '''https://pubtables1m.blob.core.windows.net/model/pubtables1m_detection_detr_r18.pth''',
         '''https://pubtables1m.blob.core.windows.net/model/pubtables1m_structure_detr_r18.pth''',
     ],
     help='''URL of the Table Transformer checkpoint you\'d like to convert.''',
 )
 parser.add_argument(
     '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the folder to output PyTorch model.'''
 )
 parser.add_argument(
     '''--push_to_hub''', action='''store_true''', help='''Whether or not to push the converted model to the 🤗 hub.'''
 )
 a       :						int       		=		parser.parse_args()
 convert_table_transformer_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)